g5_selecting_model.py

#train & test split
tx_class = tx_class.drop('NextPurchaseDay',axis=1)
X, y = tx_class.drop('NextPurchaseDayRange',axis=1), tx_class.NextPurchaseDayRange
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=44)

#create an array of models
models = []
models.append(("LR",LogisticRegression()))
models.append(("NB",GaussianNB()))
models.append(("RF",RandomForestClassifier()))
models.append(("SVC",SVC()))
models.append(("Dtree",DecisionTreeClassifier()))
models.append(("XGB",xgb.XGBClassifier()))
models.append(("KNN",KNeighborsClassifier()))

#measure the accuracy 
for name,model in models:
    kfold = KFold(n_splits=2, random_state=22)
    cv_result = cross_val_score(model,X_train,y_train, cv = kfold,scoring = "accuracy")
    print(name, cv_result)